Authors:
Angelica Muñoz-Meléndez
1
;
Pritviraj Dasgupta
2
and
William Lenagh
2
Affiliations:
1
National Institute of Astrophysics, Optics, and Electronics and University of Nebraska at Omaha, Mexico
;
2
University of Nebraska at Omaha, United States
Keyword(s):
Robot Team, Task Allocation, Demand, Stochastic Queue.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Autonomous Agents
;
Collective and Social Robots
;
Distributed Control Systems
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Robotics and Automation
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
A central problem in multi-robot systems is to solve the multi-robot task allocation problem. In this paper, a
decentralized stochastic model based on stochastic queueing processes is applied for an application of collective
detection of underground landmines where the robots are not told the distribution or number of landmines
to be encountered in the environment. Repeat demands of inspection in the environment to ensure the accuracy
of robot findings are necessary in this application. The proposed model is based on the estimation of a
stochastic queue of pending demands that represents the alternatives of action for a robot and is used to negotiate
possible conflicts with other robots. We compare and contrast this method with a decentralized greedy
approach based on the distance towards the sites where inspection demands are required. Experimental results
obtained using simulated robots in the Webots c
environment are presented. The performance of robots is
measured in terms of tw
o metrics, completion time and distance traveled for processing a demand. Robots
applying the stochastic queueing model obtained competitive results.
(More)